Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model
We propose a heterogeneous graph mamba network (HGMN) as the first exploration in leveraging the selective state space models (SSSMs) for heterogeneous graph learning. Compared with the literature, our HGMN overcomes two…
Graph LearningMambaState Space ModelsText-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
Given the ubiquity of graph data, it is intriguing to ask: Is it possible to train a graph foundation model on a broad range of graph data across diverse domains? A major hurdle toward this goal lies in the fact that gra…
Graph LearningInterpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevai…
Graph LearningTowards Graph Contrastive Learning: A Survey and Beyond
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive n…
Contrastive LearningData AugmentationDrug DiscoveryGraph Learning+5Perception-Inspired Graph Convolution for Music Understanding Tasks
We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensio…
Graph ClassificationGraph LearningLink PredictionNode Classification+1Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to address the challenges of ever-growing re…
Adversarial AttackGraph LearningA Survey of Large Language Models for Graphs
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, suc…
Graph LearningLink PredictionNode ClassificationSurveyImbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and i…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+1Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic pr…
EEGGraph ClassificationGraph LearningGraph Neural Network+2Acceleration Algorithms in GNNs: A Survey
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up to real-world and large-scale graph appl…
Graph LearningSurveyFederated Graph Condensation with Information Bottleneck Principles
Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks. However, existing GC methods rely on c…
Graph LearningInference AttackMembership Inference AttackPrivacy PreservingSemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs
Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relatio…
Autonomous DrivingGraph LearningKnowledge GraphsTrajectory PredictionFederated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastruc…
Demand ForecastingFederated LearningGraph LearningGraph Neural NetworkBridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various applications, such as social network analy…
Drug DiscoveryFairnessGraph LearningGraph Neural Network+1FairGT: A Fairness-aware Graph Transformer
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passi…
Fairnessfeature selectionGraph LearningCoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic. Current advanced neural models for stance detection typically employ fully parametric s…
Graph LearningStance DetectionAre Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominantly have an …
Graph ClassificationGraph EmbeddingGraph LearningGraph Neural Network+4CNN2GNN: How to Bridge CNN with GNN
Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convol…
Graph LearningInductive LearningA Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recen…
Graph LearningTime-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods. These methods typically require extensive data fo…
Graph LearningManagement